An Efficient Metric for Physical-layer Jammer Detection in Internet of Things Networks

Abdollahi, Mostafa; Malekinasab, Kousar; Tu, Wanqing; Bag-Mohammadi, Mozafar

doi:10.1109/lcn52139.2021.9524936

Cited by 4 publications

(10 citation statements)

References 22 publications

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“…In [35], the authors located the jammed nodes in broadcast networks by developing an algorithm named the number of jammed slots (NJS), which collects the MAC-layer statuses of the jammed nodes at the software level. The NJS increases the number of jammed slots in each node if the node is receiving a meaningless signal from an illegitimate wireless device.…”

Section: B Localizationmentioning

confidence: 99%

An Efficient Localization and Avoidance Method of Jammers in Vehicular Ad Hoc Networks

et al. 2022

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Jamming is a terrifying attack that could harm 802.11p-based vehicular communications by occupying the communication channels by overwhelming the network with jamming packets, especially for self-driving cars, as it is essential to send/receive messages without any interruptions to control the vehicles remotely. In wireless vehicular ad hoc networks (VANET), the attacker's mission is more accessible due to the network's open nature, way of communication, and lack of security measures. Most of the existing studies have focused on jamming detection approaches. However, few of them have addressed the jammer localization challenge. Moreover, even in these limited studies, the solutions' assumptions, the proposed countermeasures, and their complexity were also missing. Therefore, this paper introduces a new approach to detecting, localizing, and avoiding jamming attacks in VANETs with high efficiency in terms of accuracy, implementation and complexity. The proposed approach uses the signal strength of the jammer for estimating only the distance between jammer and receiver, while then a less complex algorithm is proposed for localizing the jammer and then redirecting the vehicles away from the roads the attacker is using. This approach was simulated using real-life maps and specialized network environments. Additionally, the performance of the new approach was evaluated using different metrics. These evaluation metrics include (1) the estimated position of the jammer, (2) the handling of the jammer by announcing its location to normal vehicles (3) the avoidance of the jammed routes by increasing their weight, which forces the cars to reroute and evade the jamming area. The high localization accuracy, measured by the Euclidean distance, and the successful communication of the attacker's position and its avoidance have highly increased the packet delivery ratio (PDR) and the signal-to-interference-plus-noise ratio (SINR). This was noticed significantly before and after avoiding the jamming area when for example, the PDR increased from 0% to 100% before and after bypassing the jammer's routes.

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Section: B Localizationmentioning

confidence: 99%

An Efficient Localization and Avoidance Method of Jammers in Vehicular Ad Hoc Networks

et al. 2022

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“…Security challenges in WSN include jamming attacks, where the jammer transmits a signal towards the target channel, intends to decrease the Signal-to-Noise Ratio (SNR); increases Bit Error Rate (BER), and decreases the Packet Delivery Ratio (PDR) which cause preventing the node from communicating with its neighbors [14,15]. Moreover, by emitting a signal with high power that is considered noise on the receiver side, nodes within the jamming area or near the noise source may not assign themselves to the available channel due to the jamming signal.…”

Section: Introductionmentioning

confidence: 99%

Received Power Based Unmanned Aerial Vehicles (UAVs) Jamming Detection and Nodes Classification Using Machine Learning

Aldosari¹

2023

Computers, Materials &Amp; Continua

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This paper presents a machine-learning method for detecting jamming UAVs and classifying nodes during jamming attacks on Wireless Sensor Networks (WSNs). Jamming is a type of Denial of Service (DoS) attack and intentional interference where a malicious node transmits a high-power signal to increase noise on the receiver side to disrupt the communication channel and reduce performance significantly. To defend and prevent such attacks, the first step is to detect them. The current detection approaches use centralized techniques to detect jamming, where each node collects information and forwards it to the base station. As a result, overhead and communication costs increased. In this work, we present a jamming attack and classify nodes into different categories based on their location to the jammer by employing a single node observer. As a result, we introduced a machine learning model that uses distance ratios and power received as features to detect such attacks. Furthermore, we considered several types of jammers transmitting at different power levels to evaluate the proposed metrics using MATLAB. With a detection accuracy of 99.7% for the k-nearest neighbors (KNN) algorithm and average testing accuracy of 99.9%, the presented solution is capable of efficiently and accurately detecting jamming attacks in wireless sensor networks.

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“…This paper was presented in part at the 2021 IEEE 46th Conference on Local Computer Networks (LCN), Edmonton, AB, Canada, October 4-7 [1] IoT network. Therefore, it is crucial to quickly and accurately detect physical-layer jammers in IoT networks.…”

Section: Introductionmentioning

confidence: 99%

“…• Comprehensive jammer detection. Physical-layer jammers can be reactive and proactive 1 . As compared to our previous work [1] that uses witness nodes to detect reactive jammers, our new NJS algorithm can efficiently and accurately detect both reactive and proactive jammers.…”

Section: Introductionmentioning

confidence: 99%

“…Physical-layer jammers can be reactive and proactive 1 . As compared to our previous work [1] that uses witness nodes to detect reactive jammers, our new NJS algorithm can efficiently and accurately detect both reactive and proactive jammers. This is achieved by enabling the jammer monitor center (JMC) to identify a void region in the network where there have been no valid communication activities for a certain time period.…”

Section: Introductionmentioning

confidence: 99%

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Physical-Layer Jammer Detection in Multihop IoT Networks

Abdollahi

Malekinasab

et al. 2023

IEEE Internet Things J.

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The presence of a jammer in an IoT network severely degrades all communication efforts between adjacent wireless devices. The situation is getting worse due to retransmission attempts made by affected devices. Therefore, jammers must be detected or localized quickly to activate a series of corrective countermeasures so as to ensure the robust operation of the IoT network. This paper proposes a novel metric called the number of jammed slots (NJS). It can detect and localize both reactive and proactive jammers that follow arbitrary jamming attack patterns. NJS is applicable to all communication paradigms such as unicast, broadcast, and multicast. In NJS, the wireless medium status is monitored by IoT devices and summarized reports are sent to a central node. Then, the central node determines the jamming duration, the affected nodes, and the approximate location of the jammer(s). Also, the specificity, precision, and accuracy of NJS are at least 48%, 19%, and 20% better than the other state-of-the-art statistical methods, respectively. In addition, in terms of the detection time, NJS is four times faster when detecting an active jammer in the network. It can also localize the jammer with less jammer localization errors.

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An Efficient Metric for Physical-layer Jammer Detection in Internet of Things Networks

Cited by 4 publications

References 22 publications

An Efficient Localization and Avoidance Method of Jammers in Vehicular Ad Hoc Networks

An Efficient Localization and Avoidance Method of Jammers in Vehicular Ad Hoc Networks

Received Power Based Unmanned Aerial Vehicles (UAVs) Jamming Detection and Nodes Classification Using Machine Learning

Physical-Layer Jammer Detection in Multihop IoT Networks

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